基于颜色校正和多尺度融合的水下图像增强OA北大核心CSTPCD

To solve the problem of color distortion and detail blur in underwater images,the algorithm in this paper takes Unet network as the basic framework,inputs multi-scale images in different coding layers at the same time,fuses feature streams between upper and lower layers to obtain better detail preservation effect,and realizes the ability of extracting details from coarse to fine.In addition,the color correction module and dual attention module are introduced to effectively solve the problem of color deviation and uneven detail recovery in underwater images.The experimental results show that PSNR and UIQM indexes of the images enhanced by the proposed algorithm on UFO,EUVP and UIEB data sets increase by 21.3%and 25.6%respectively,compared with the original images.This algorithm can effectively improve the visual quality of underwater images,and is superior to other algorithms in subjective visual and objective evaluation indexes.